A reference-decoupled reformulation makes direct data-driven LQT equivalent to certainty-equivalence solutions and supports convergent offline and online DeePO algorithms.
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3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3representative citing papers
A convex data-driven inverse RL framework for linear systems with uncertainty that uses a generalized LQR cost with cross terms, kernel regression from data, and differentiable SDPs for robust cost design over perturbations.
Local linearity of LLM layers enables LQR-based closed-loop activation steering with theoretical tracking guarantees.
citing papers explorer
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Direct Data-Driven Linear Quadratic Tracking via Policy Optimization
A reference-decoupled reformulation makes direct data-driven LQT equivalent to certainty-equivalence solutions and supports convergent offline and online DeePO algorithms.
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Data-Driven Inverse Reinforcement Learning of Linear Systems with Model Uncertainty: A Convex Optimization View
A convex data-driven inverse RL framework for linear systems with uncertainty that uses a generalized LQR cost with cross terms, kernel regression from data, and differentiable SDPs for robust cost design over perturbations.
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Local Linearity of LLMs Enables Activation Steering via Model-Based Linear Optimal Control
Local linearity of LLM layers enables LQR-based closed-loop activation steering with theoretical tracking guarantees.